Trade-space exploration with data preprocessing and machine learning for satellite anomalies reliability classification

Mutholib, Abdul and Abdul Rahim, Nadirah and Gunawan, Teddy Surya and Kartiwi, Mira (2025) Trade-space exploration with data preprocessing and machine learning for satellite anomalies reliability classification. IEEE Access, 13. pp. 35903-35921. E-ISSN 2169-3536

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Abstract

Satellite reliability is critical to ensuring uninterrupted operations in aerospace systems, where anomalies can lead to mission failures and significant economic losses. Existing anomaly classification methods often lack scalability, interpretability, and adaptability to diverse datasets. This study introduces the Trade-Space Exploration Machine Learning (TSE-ML) framework, a comprehensive pipeline for satellite anomaly classification that optimizes preprocessing, transformation, normalization, and machine learning stages. Leveraging a Seradata dataset spanning 66 years and 4,455 satellite records, the framework systematically evaluates four data cleaning methods, four data transformation techniques, five normalization strategies, and seven machine learning algorithms across 480 configurations. The optimal configuration, comprising Iterative Imputation, FastText, Robust Scaling, and Decision Tree, achieved the highest testing accuracy of 95.74% with competitive computational efficiency. The Decision Tree model delivered superior accuracy and provided interpretability, revealing critical factors influencing satellite anomalies, such as Age Since Launch, Design Life, and Orbit Category. Stratified 5-fold cross-validation ensured robustness and generalizability of the results. The TSE-ML framework’s transparency and high performance enable actionable insights for improving satellite design, operational planning, and anomaly mitigation. Future research will focus on real-time anomaly detection, integrating satellite telemetry data, and extending the framework to other space applications. This study establishes a robust, interpretable foundation for advancing anomaly classification in aerospace engineering, addressing the dual challenges of reliability and operational efficiency.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Satellite anomaly detection, satellite reliability classification, trade-space exploration, data preprocessing techniques, machine learning models, Seradata dataset, decision support systems.
Subjects:	T Technology > T Technology (General) T Technology > T Technology (General) > T55.4 Industrial engineering.Management engineering. T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Engineering
Depositing User:	Dr Nadirah Abdul Rahim
Date Deposited:	26 Mar 2025 15:46
Last Modified:	26 Mar 2025 15:46
URI:	http://irep.iium.edu.my/id/eprint/120408

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